Analyzing Relationships Between School Libraries and Academic Achievement

1
Analyzing Relationships Between School Libraries
and Academic Achievement

• Keith Curry Lance
• Director
• Library Research Service
• Colorado State Library University of Denver

2
Outline

• Background
• Research questions
• Data types sources
• Statistical concepts techniques
• Success stories

3
Background

• A half century of previous school library
  research
• The political climate of education libraries in
  the late 80s
• The School Match Incident
• The first Colorado study
• The political climate of education libraries in
  the late 90s
• The second Colorado study successor studies by
  Lance, Rodney Hamilton-Pennell
• Successor studies by others

4
Research Questions

• Are students more likely to pass tests if they
  have a school library than if they dont?
• Are students likely to score higher on tests if
  they have a school library than if they dont?
• As the school library improves, do test scores
  rise?

• How are different qualities of school libraries,
  schools, and communities related to each other?
• Do school libraries test scores improve
  together, even when other school community
  conditions are taken into account?

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Types of Data

• Nominal
• Categories
• No necessary quantitative dimension
• Pass/fail, library/no library
• Ordinal
• Degrees of difference
• No equal intervals
• Zero is just a code
• Usually limited number of values

• Interval/Ratio
• Equal intervals
• True zero (have none of something)
• Usually large number of values
• Weekly hours of librarian staffing, test scores

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Types of Variables

• Dependent variable
• The effect in a cause-and-effect relationship
• Reading test scores used to operationalize
  concept of academic achievement

• Independent variables
• The causes in a cause-and-effect relationship
• Characteristics of school libraries, schools
  communities
• Treatment or predictor variables
• Control variables

7
State Test Scores

• Standards-based tests v. standardized tests
• Test scores, proficient above v. passed
  v. percentile rankings
• Reading scores are key
• Difference between existing available data
  (actually acquiring data file in a usable format
  on a timely schedule)

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Other Data Sources
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The Data Model
Community
School library
School
Test scores
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Experiment v. Statistical Analysis

• Experiment
• Older studies
• Smaller samples
• More precise units of analysis (student)
• More control over independent variables
• Matching issues
• Easier to explain, communicate

• Statistical analysis
• Newer studies
• Larger samples
• Less precise units of analysis (school)
• Less control over independent variables
• Data availability issues
• More precise measurement of effects

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Statistical Significance

• Likelihood the sample results are representative
  of the universe under study
• Most common notation
• p lt .05, lt .01, lt .001
• Difference between statistical significance
  confidence interval (i.e., margin of error)
• No statistical test of SUBSTANTIVE significance
  (i.e., how important is this?)

12
Statistical Analysis Software

• Market leaders
• SPSS Statistical Package for the Social Sciences
• SAS Statistical Analysis Software
• Software Issues
• Available statistical techniques correlation,
  comparison of means, factor analysis, regression
• Data management features sort, sample, compute,
  recode, if
• Case limits (maximum number of cases allowed)
• Cost (education discount)

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Cross-tabulation

• Are students more likely to pass tests if they
  have a school library than if they dont?
• Two nominal variables or one nominal and one
  ordinal (small range)
• Pass/fail on tests, librarian/no librarian
• Turning interval or ratio variables into nominal
  or ordinal ones
• Chi-square (X2) indicates statistical
  significance

14
Test Scores by Time Spent Teaching Information
Literacy Alaska, 1998
Chi-square 12.743, p lt .001
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Comparison of Means

• Are students likely to score higher on tests if
  they have a school library than if they dont?
• One nominal (2 dimensions), one interval or ratio
  variable
• Pass/fail on test, hours of librarian staffing
• Generates means (averages) for 2 groups
• Levenes test indicates equality (or inequality)
  of variances between groups
• t test indicates statistical significance of
  difference between groups

16
Student Visits for Information Literacy
Instruction for Higher Lower Scoring Elementary
Schools Alaska, 1998
t 3.963, p lt .001
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Correlation (r)

• As the school library improves, do test scores
  rise?
• Two interval or ratio variables
• LM expenditures per student, volumes per student
• Pearsons product-moment correlation (r)
• Expressed in decimal form
• Perfect correlation 1.00
• - indicate positive negative relationships
  ( both rise or fall, - one rises, other
  falls)
• r .60-.80 v. .80 factor analysis
• r square percent of variation explained

18
Bivariate Correlation Coefficients for LM Program
Development Variables Colorado Middle Schools,
1999
p lt .001
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Factor Analysis

• How are different qualities of school libraries
  (schools, communities) related to each other?
• Analyzes relationships between and among
  variables
• Key statistics
• Percent of variance explained
• Factor loadings
• Factor scores
• Allow mixing items on different scales
• Data reduction technique

20
Factor Analysis of LM Program Development
Variables Colorado Middle Schools, 1999
Initial eigenvalue 4.638, 77 variance explained
21
Regression (R, R2)

• Do school libraries test scores improve
  together, even when other conditions are taken
  into account?
• Need to conduct correlationand often
  factoranalyses first
• Linear regression
• Stepwise regression
• Multiple R, R square R square change
• Standardized beta coefficients (indicate positive
  or negative direction)
• Included v. excluded variables

22
Regression Analysis of 4th Grade Scores with LM,
School, Community Predictors Colorado, 1999
p lt .01 Excluded variables teacher-pupil ratio,
per pupil expenditures, teacher characteristics
23
Success Stories

• Even the strongest statistical evidence can be
  made more persuasive by compelling success
  stories

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Characteristics of Good Success Stories

• One clear point value of librarian as teacher
  (technology coordinator, in-service provider)
• Variety of voices librarians, students,
  teachers, principals, parents
• Short sweet
• A quotable quote